DeepLearning.AI and Coursera launched a specialization covering math, probability, linear algebra, and optimization for machine learning. The program includes interactive exercises and labs designed to help you pass interviews and build practical skills in areas where most candidates struggle.
I am excited to share that the Mathematics for Machine Learning and Data Science specialization from Deep Learning AI is available at Coursera. Mathematics can be a sticking point for people at all stages of their AI career, from beginners to experienced practitioners. I've heard many stories from candidates who go through multiple rounds of interviews only to get rejected for a math or optimization question, or from learners who are hesitant to pursue machine learning due to the mathematics requirements. I've designed the Mathematics for Machine Learning and Data Science specialization to build that foundation in mathematics and to enable you to take your skills and career to the next level. In this specialization, you will learn the math and optimization methods behind many machine learning and data science algorithms, as well as how to use probability to calculate the uncertainty on your model's output. You will also learn to assess your model's performance using confidence interval and statistical hypothesis testing as well as linear algebra techniques to structure and transform your data. Throughout this specialization, we provide you with interactive exercises so you can manipulate concepts in a visual way. You'll apply your skills to machine learning and data science. Use cases through hands on lab activities. We are excited to support your growth in machine learning and data science. Enroll today and start learning.
